Brain Tumor Detection

Dataset Description

Abstract

A Brain tumor is considered as one of the aggressive diseases, among children and adults. Brain tumors account for 85 to 90 percent of all primary Central Nervous System(CNS) tumors. Every year, around 11,700 people are diagnosed with a brain tumor. The 5-year survival rate for people with a cancerous brain or CNS tumor is approximately 34 percent for men and36 percent for women. Brain Tumors are classified as: Benign Tumor, Malignant Tumor, Pituitary Tumor, etc. Proper treatment, planning, and accurate diagnostics should be implemented to improve the life expectancy of the patients. The best technique to detect brain tumors is Magnetic Resonance Imaging (MRI). A huge amount of image data is generated through the scans. These images are examined by the radiologist. A manual examination can be error-prone due to the level of complexities involved in brain tumors and their properties. The application of automated classification techniques using Machine Learning(ML) and Artificial Intelligence(AI)has consistently shown higher accuracy than manual classification. Hence, proposing a system performing detection and classification by using Deep Learning Algorithms using Convolution-Neural Network (CNN), Artificial Neural Network (ANN), and Transfer-Learning (TL) would be helpful to doctors all around the world.

Context

Brain Tumors are complex. There are a lot of abnormalities in the sizes and location of the brain tumor(s). This makes it really difficult for a complete understanding of the nature of the tumor. Also, a professional Neurosurgeon is required for MRI analysis. Oftentimes in developing countries, the lack of skillful doctors and lack of knowledge about tumors makes it really challenging and time-consuming to generate reports from MRI. So an automated system on Cloud can solve this problem.

Definition

To Detect and Classify Brain Tumor using, CNN and TL; as an asset of Deep Learning and to examine the tumor position(segmentation).

About the data:

The dataset contains 3 folders: yes, no, and pred which contains 3060 Brain MRI Images.

Folder Description

Folder Description
Yes The folder yes contains 1500 Brain MRI Images that are tumorous
No The folder no contains 1500 Brain MRI Images that are non-tumorous

Dataset Distribution

Modeling: Keras Multi-layer Perceptron (MLP) for Image Classifications

A multi-layer perceptron (MLP) is a class of feedforward artificial neural network (ANN). The algorithm at each iteration uses the RMSprop algorithm to measure the loss, and then the gradient and the model update is calculated. At the end of this iterative process, we would reach a better level of agreement between test and predicted sets since the error would be lower from that of the first step.

Compiling and fitting the model


References

  1. Kaggle Dataset: Brain Tumor Detection
  2. Tensorflow API Documentation